Essays on supplier responsiveness and buyer firm value - Nyenrode ...

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2.6 The Structural Model The second part in our model estimation is assessing the structural model. The SEM technique was chosen because it is an important tool for academic research as well as for analyzing survey data and covariance structures in data (Hair et al., 2006). We follow the seven stages of structural equation modeling that were developed by Hair et al. (2006). The first stage is developing a modeling strategy. We had two alternative strategies from which to choose while developing our modeling strategy. The first alternative is developing a single model development strategy (i.e., also called a confirmatory model strategy) and the second alternative is developing a competing model strategy. The benefit of the first alternative is that it leads a researcher to focus on one model and, in turn, this leads to a rigorous application of the SEM technique. In tandem, a single model development strategy suffers from the lack of identification of alternative models (Hair et al., 2006). The benefit of the second alternative is that it allows a researcher to assess alternative theories, but we had already evaluated alternative theories during our literature review and synthesis. Moreover, we have already arrived at a specific model that we wanted to test. Hence, a single model strategy best suited our current needs. We also dealt with whether we had enough evidence to determine causality. The most important criterion is a sound theoretical reasoning that we have developed in the earlier section on our hypotheses development and literature synthesis. Supplier responsiveness, IdRR, buyer satisfaction, supplier brand value, and buyer firm value have statistical associations and cause and effect antecedence based on prior research that was covered in the literature synthesis. We chose the most important variables after our literature synthesis, thus significantly reducing the possibility of a lack of alternative causal variables. A limitation of this approach is that we are not able to investigate all other possible models that our research might include. While specifying the initial model we had to adhere to the rule of five observations for each measured variable in the model (Hair et al., 2006, p. 166). Our initial model with 39 items and four latent constructs and one observed construct did not meet this criterion. With the help of modification indices, this was later reduced to 10 measured variables and five constructs. This gave us a ratio of about 16 respondents for every variable (164/10), which exceeds the minimum recommended limit of five respondents for every item (Hair et al., 2006, p. 166). Our four latent concepts and one observed construct are fewer than the maximum recommended of 20 for using the SEM technique. In the second and third stages, we constructed a path diagram of causal relationships. We have one exogenous construct, which is supplier responsiveness. We have four endogenous constructs, namely IdRR, satisfaction, supplier brand value, and buyer firm value. We also tested for unidimensionality. Our results indicated that our constructs load on more than one factor and, therefore, unidimensionality is not a concern in the current study. In the fourth stage of our research, we determined the input matrix type and estimated the proposed model. We choose the covariance matrix because it is a true test of theory (Hair et al., 2006, p. 603). The correlation matrix is not suited for theory testing as it only represents a pattern of relationships. We used the maximum likelihood estimation method and our sample size of 164 was above the recommended 64
ange of 100-150 (Hair et al., 2006, p. 605). In AMOS we also selected covariance supplied and to be analyzed as the maximum likelihood option and we selected the option random permutations. In the fifth stage, we assessed our structural model. Model identification is measured in AMOS by the difference between the number of distinct sample moments (55) and the distinct parameters to be estimated (25). The resulting degrees of freedom 30 indicate that the model has been identified. We achieved a good model fit and identification with 10 items, as shown in Table 2.23: Table 2.23 Structural Model Indices Goodness of fit index Recommended Values Values χ 2 /degrees of freedom ≤ 3.00 1.83 Tucker Lewis Index (TLI) ≥ 0.90 0.93 Goodness-of-Fit Index (GFI) ≥ 0.90 0.94 Incremental Fit Index (IFI) ≥ 0.90 0.95 Comparative Fit Index (CFI) ≤ 0.90 0.95 Root Mean Square Error of Approximation (RMSEA) ≤ 0.08 0.072 In stage six, we interpret our model. The fit statistics for our model are good. The data supported our first hypothesis and show that supplier responsiveness is an antecedent to IdRR. The constructs are negatively related: β=-0.988, p
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ISBN 978-90-8980-055-8 © M. Abrahi
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Examination Committee Supervisor: P
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2.3 DEVELOPMENT OF THE CONCEPTUAL M
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5.1 RESEARCH FINDINGS 114 5.2 OVERA
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List of Tables Table 2.1 Representa
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PREFACE Oscar Wilde once said, “I
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Page 16 and 17: Figure 1.1 Mckinsey Survey Results
Page 18 and 19: 1.4 Scope PhD thesis This thesis fo
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Page 59 and 60: 2.5 Measurement Model For creating
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uyer firm performance through a ser
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specifically highlighted the import
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5.5 Practical Implications From a m
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Summary in English This dissertatio
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is that, when developing marketing
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isk by 0.98 standard deviations. Su
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collectivist culture and a Confucia
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Overwegende dat Verhoef en Leeflang
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van de waarde te communiceren dat d
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erkennen dat dit niet altijd een mo
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ij de vraag of cultureel risico voo
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Businessweek. (2012). S&P/BusinessW
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-industry-09012011.html Grewal, R.,
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Langerak, F. (2001). Effects of mar
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Quelch, J. (2007). How to brand an
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Verhoef, P. C., & Leeflang, P. S. H
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1. The name of the company where yo
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9.The supplier seems to have good i
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17. The supplier takes a very long
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25. If the supplier were to find ou
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33.Does your strategic supplier als
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41.To what degree do those specific
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Supplier Brand Value 51.How likely
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Appendix B Scatter Plot Diagrams fo
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